Jonathan Ullman

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Citations	4231	3177
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Adam SmithProfessor of Computer Science, Boston UniversityVerified email at bu.edu
Thomas SteinkeGoogle Research, Brain TeamVerified email at google.com
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Salil VadhanVicky Joseph Professor of Computer Science and Applied Mathematics, Harvard UniversityVerified email at g.harvard.edu
Gautam KamathAssistant Professor @ University of Waterloo, Faculty Member @ Vector InstituteVerified email at uwaterloo.ca
Albert CheuPostdoctoral Fellow, Georgetown UniversityVerified email at georgetown.edu
Kobbi NissimDept. of Computer Science (Georgetown)Verified email at georgetown.edu
Alina OpreaNortheastern UniversityVerified email at neu.edu
Matthew JagielskiGoogle ResearchVerified email at google.com
Cynthia DworkMicrosoft ResearchVerified email at microsoft.com
Vikrant SinghalPostdoctoral Fellow, University of WaterlooVerified email at northeastern.edu
Zhiwei Steven WuCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Michael KearnsProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Mallesh M. PaiRice UniversityVerified email at rice.edu
Raef BassilyAssistant Professor, Computer Science & Engineering, The Ohio State UniversityVerified email at osu.edu
Justin ThalerGeorgetown UniversityVerified email at georgetown.edu
Lydia ZakynthinouPhD Student, Northeastern UniversityVerified email at northeastern.edu
Anupam GuptaProfessor of Computer Science at Carnegie Mellon UniversityVerified email at cs.cmu.edu
Uri StemmerTel Aviv UniversityVerified email at tau.ac.il
Moritz HardtMax Planck Institute for Intelligent SystemsVerified email at is.mpg.de

Jonathan Ullman

Associate Professor of Computer Science, Northeastern University

Verified email at ccs.neu.edu - Homepage

Differential Privacy Machine Learning Theory Cryptography


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Distributed Differential Privacy via Shuffling A Cheu, A Smith, J Ullman, D Zeber, M Zhilyaev	285	2018
Algorithmic stability for adaptive data analysis R Bassily, K Nissim, A Smith, T Steinke, U Stemmer, J Ullman Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016	270	2016
Exposed! a survey of attacks on private data C Dwork, A Smith, T Steinke, J Ullman Annual Review of Statistics and Its Application 4, 61-84, 2017	203	2017
Iterative constructions and private data release A Gupta, A Roth, J Ullman Theory of Cryptography: 9th Theory of Cryptography Conference, TCC 2012 …, 2012	200	2012
Fingerprinting codes and the price of approximate differential privacy M Bun, J Ullman, S Vadhan SIAM Journal on Computing 47 (5), 1888-1938, 2018	183*	2018
Robust traceability from trace amounts C Dwork, A Smith, T Steinke, J Ullman, S Vadhan 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 650-669, 2015	168	2015
Robust mediators in large games M Kearns, MM Pai, R Rogers, A Roth, J Ullman arXiv preprint arXiv:1512.02698, 2015	166*	2015
Privately releasing conjunctions and the statistical query barrier A Gupta, M Hardt, A Roth, J Ullman Proceedings of the forty-third annual ACM symposium on Theory of computing …, 2011	156	2011
Between pure and approximate differential privacy T Steinke, J Ullman arXiv preprint arXiv:1501.06095, 2015	132	2015
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows SP Kasiviswanathan, M Rudelson, A Smith, J Ullman Proceedings of the forty-second ACM symposium on Theory of computing, 775-784, 2010	124	2010
Differentially private fair learning M Jagielski, M Kearns, J Mao, A Oprea, A Roth, S Sharifi-Malvajerdi, ... International Conference on Machine Learning, 3000-3008, 2019	120	2019
Preventing false discovery in interactive data analysis is hard M Hardt, J Ullman Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on …, 2014	119	2014
Auditing differentially private machine learning: How private is private sgd? M Jagielski, J Ullman, A Oprea Advances in Neural Information Processing Systems 33, 22205-22216, 2020	112	2020
PCPs and the hardness of generating private synthetic data J Ullman, S Vadhan Theory of Cryptography: 8th Theory of Cryptography Conference, TCC 2011 …, 2011	108*	2011
Interactive fingerprinting codes and the hardness of preventing false discovery T Steinke, J Ullman Conference on learning theory, 1588-1628, 2015	105	2015
Answering n^{2+o(1)} counting queries with differential privacy is hard J Ullman SIAM Journal on Computing 45 (2), 473-496, 2016	102	2016
Faster algorithms for privately releasing marginals J Thaler, J Ullman, S Vadhan Automata, Languages, and Programming: 39th International Colloquium, ICALP …, 2012	97	2012
Privately learning high-dimensional distributions G Kamath, J Li, V Singhal, J Ullman Conference on Learning Theory, 1853-1902, 2019	93	2019
Local differential privacy for evolving data M Joseph, A Roth, J Ullman, B Waggoner Advances in Neural Information Processing Systems 31, 2018	78	2018
PSI: a Private data Sharing Interface M Gaboardi, J Honaker, G King, K Nissim, J Ullman, S Vadhan arXiv preprint arXiv:1609.04340, 2016	77	2016

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